{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras import Model, layers, activations\n",
    "\n",
    "from plot_utils import plot_cost_accuracy, plot_decision_boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = datasets.make_blobs(n_samples=100000, n_features=2, centers=3, random_state=0)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_test, y_test, test_size=0.5, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((80000, 2), (80000,), (10000, 2), (10000,), (10000, 2), (10000,))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape, X_val.shape, y_val.shape, X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2359081dfc8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_train[:1000, 0], X_train[:1000, 1], s=40, c=y_train[:1000], cmap=plt.cm.Spectral)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MLP(Model):\n",
    "    def __init__(self, num_classes=3):\n",
    "        super(MLP, self).__init__()\n",
    "        self.hidden_layer1 = layers.Dense(3, activation=activations.relu)\n",
    "        self.hidden_layer2 = layers.Dense(5, activation=activations.relu)\n",
    "        self.hidden_layer3 = layers.Dense(4, activation=activations.relu)\n",
    "        self.output_layer = layers.Dense(num_classes, activation=activations.softmax)\n",
    "\n",
    "    def call(self, inputs, training=None, **kwargs):\n",
    "        x = self.hidden_layer1(inputs)\n",
    "        x = self.hidden_layer2(x)\n",
    "        x = self.hidden_layer3(x)\n",
    "        x = self.output_layer(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "@tf.function\n",
    "def _train_step(x, y, model, optimizer, loss_object, train_loss, train_accuracy):\n",
    "    with tf.GradientTape() as tape:\n",
    "        y_pred = model(x, training=True)\n",
    "        # print(\"=> label shape: \", y.shape, \"pred shape\", y_pred.shape)\n",
    "        loss = loss_object(y, y_pred)\n",
    "    gradients = tape.gradient(loss, model.trainable_variables)\n",
    "    optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
    "    train_loss(loss)\n",
    "    train_accuracy(y, y_pred)\n",
    "\n",
    "\n",
    "@tf.function\n",
    "def _val_step(x, y, model, val_accuracy):\n",
    "    y_pred = model(x)\n",
    "    val_accuracy(y, y_pred)\n",
    "\n",
    "\n",
    "def build_model(train_data, val_data, learning_rate=0.001, epochs=100, print_cost=False):\n",
    "    # keep track of cost\n",
    "    costs = []\n",
    "    train_accs = []\n",
    "    val_accs = []\n",
    "\n",
    "    model = MLP()\n",
    "    # Stochastic gradient descent optimizer.\n",
    "    optimizer = tf.optimizers.SGD(learning_rate)\n",
    "\n",
    "    loss_object = tf.keras.losses.SparseCategoricalCrossentropy()\n",
    "    train_loss = tf.keras.metrics.Mean(name='train_loss', dtype=tf.float32)\n",
    "    train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')\n",
    "    val_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='val_accuracy')\n",
    "\n",
    "    for i in range(epochs):\n",
    "        for x_, y_ in train_data:\n",
    "            _train_step(x_, y_, model, optimizer, loss_object, train_loss, train_accuracy)\n",
    "\n",
    "        for x_, y_ in val_data:\n",
    "            _val_step(x_, y_, model, val_accuracy)\n",
    "\n",
    "        if print_cost and (i + 1) % 10 == 0:\n",
    "            template = \"Cost after iteration {}: {}, Accuracy on train data: {}, Accuracy on validation data: {}\"\n",
    "            print(template.format(i + 1, train_loss.result(), train_accuracy.result(), val_accuracy.result()))\n",
    "\n",
    "        costs.append(train_loss.result())\n",
    "        train_accs.append(train_accuracy.result())\n",
    "        val_accs.append(val_accuracy.result())\n",
    "    \n",
    "    return model, costs, train_accs, val_accs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer mlp is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "Cost after iteration 10: 0.49256476759910583, Accuracy on train data: 0.8514400124549866, Accuracy on validation data: 0.8751500248908997\n",
      "Cost after iteration 20: 0.37474268674850464, Accuracy on train data: 0.881763756275177, Accuracy on validation data: 0.8953199982643127\n",
      "Cost after iteration 30: 0.3251858949661255, Accuracy on train data: 0.8938841819763184, Accuracy on validation data: 0.9037366509437561\n",
      "Cost after iteration 40: 0.29886382818222046, Accuracy on train data: 0.9000256061553955, Accuracy on validation data: 0.9081050157546997\n",
      "Cost after iteration 50: 0.28254377841949463, Accuracy on train data: 0.9037489891052246, Accuracy on validation data: 0.910647988319397\n",
      "Cost after iteration 60: 0.2714228928089142, Accuracy on train data: 0.9062314629554749, Accuracy on validation data: 0.9123416543006897\n",
      "Cost after iteration 70: 0.26335597038269043, Accuracy on train data: 0.9080103635787964, Accuracy on validation data: 0.9135557413101196\n",
      "Cost after iteration 80: 0.2572270631790161, Accuracy on train data: 0.909347653388977, Accuracy on validation data: 0.9144487380981445\n",
      "Cost after iteration 90: 0.2524143159389496, Accuracy on train data: 0.910378634929657, Accuracy on validation data: 0.9151766896247864\n",
      "Cost after iteration 100: 0.248533695936203, Accuracy on train data: 0.9112171530723572, Accuracy on validation data: 0.9157519936561584\n"
     ]
    }
   ],
   "source": [
    "train_data = tf.data.Dataset.from_tensor_slices((X_train, y_train)).shuffle(80000, reshuffle_each_iteration=True).batch(32)\n",
    "val_data = tf.data.Dataset.from_tensor_slices((X_val, y_val)).batch(32)\n",
    "\n",
    "model_trained, costs, train_accs, val_accs = build_model(train_data, val_data, learning_rate=0.001, epochs=100, print_cost=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cost_accuracy(costs, train_accs, val_accs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_plot, y_plot = X_test[:1000, :].copy(), y_test[:1000].copy()\n",
    "X_plot, y_plot = X_plot.T, y_plot.reshape((1, y_plot.shape[0]))\n",
    "plot_decision_boundary(lambda x: model_trained(x), X_plot, y_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=float32, numpy=0.9209>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = tf.data.Dataset.from_tensor_slices((X_test, y_test)).batch(32)\n",
    "test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')\n",
    "for x_, y_ in val_data:\n",
    "    y_pred = model_trained(x_)\n",
    "    test_accuracy(y_, y_pred)\n",
    "test_accuracy.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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